Opinion summarisation aims to summarise the salient information and opinions presented in documents such as product reviews, discussion forums, and social media texts into short summaries that enable users to effectively understand the opinions therein. Generating biased summaries has the risk of potentially swaying public opinion. Previous studies focused on studying bias in opinion summarisation using extractive models, but limited research has paid attention to abstractive summarisation models. In this study, using political bias as a case study, we first establish a methodology to quantify bias in abstractive models, then trace it from the pre-trained models to the task of summarising social media opinions using different models and adaptation methods. We find that most models exhibit intrinsic bias. Using a social media text summarisation dataset and contrasting various adaptation methods, we find that tuning a smaller number of parameters is less biased compared to standard fine-tuning; however, the diversity of topics in training data used for fine-tuning is critical.

通过对抽象摘要模型进行偏见定量化研究，并应用不同的模型和适应方法来总结社交媒体意见的任务，本研究发现大多数模型具有固有偏见。使用社交媒体文本摘要数据集并对比各种适应方法，发现调整较少的参数不如标准微调偏见较小；然而，用于微调的训练数据中的主题多样性至关重要。

从预训练到适应过程中的观点摘要偏倚：以政治偏倚为案例研究